Remember the scene in the movie Total Recall, where Quaid/Arnold Schwarzenegger is trying to escape from people trying to kill him and hops into a Johnny Cab? He shouts "Anywhere, just go. Go!" and the cab drives away. Johnny Cab is an example of autonomous driving.

The most advanced varieties of autonomous driving vehicles can move and make decisions without a human at the wheel. They have cameras, sensors, and onboard software to understand their surroundings, respond to traffic signals, and react to other vehicles or pedestrians.

Grading Levels of Autonomous Driving

How advanced? There's a globally recognized system of grading exactly how automated driving is, designed by US-based SAE International.

Level 0: No automation.
Level 1: Driver assistance, with a single automated function such as adaptive cruise control.
Level 2: Partial automation, where the vehicle can steer and control speed, but with the human driver monitoring at all times.
Level 3: Conditional automation, where the vehicle can drive itself in limited situations, but may ask the human driver to take control.
Level 4: High automation, where the vehicle can drive itself in defined areas and conditions, with no human driver required.
Level 5: Full automation: the vehicle can drive anywhere, under any conditions, by itself.

Why It's Big In Japan

The technology is almost certainly going to be used heavily in Japan, with its depopulation of rural and regional areas, the ageing nature of those remaining, and the lack of younger workers to keep services running. But not just Japan; many parts of the world have similar demographic issues that require plans for the future. Bus routes could be cut when there are too few drivers to operate them. Deliveries may become less frequent with falling demand. People face the prospect of being cut off from society.

Autonomous vehicles offer a solution and could potentially make it possible for a small number of operators to oversee many vehicles at once, even across wide areas.

... But It Needs Reliable Comms

But there's a problem: constant, reliable communication is a must. Vehicle operators need live camera images, sensor data, and system status information to know what the vehicle is doing and be able to step in when necessary. No communication? No service.

Vehicles are always moving, obviously, and wireless conditions change as they go near tall buildings, under bridges, or venture close to the edge of coverage areas. Existing communication systems tend to deal with these issues after they occur, making adjustments or switching networks only after the system has started to stop working.

The Solution

NTT’s Communication Stabilization Solution for autonomous driving gets to work before problems show up. Instead of waiting for connections to degrade, the Solution, as NTT calls it, predicts how wireless conditions are likely to change along a vehicle’s route. Using machine‑learning‑based wireless quality prediction, it estimates how different networks are likely to perform in the moments ahead.

Its predictions are then used to work out how data is sent across different wireless links at the same time. Rather than relying on a single connection, the vehicle can use several: public mobile networks, local 5G, and Wi‑Fi. As conditions change, the system adjusts how much data flows over each link. If one link starts to fade out, others can take up the slack. The Solution has a real‑time data pipeline that takes in camera images, sensor readings, and other vehicle data and delivers them to a remote monitoring center, giving human operators a continuous view of the vehicle and its surroundings.

Demonstration trials have already shown the strength of the SAE Level 4 Solution. When wireless quality prediction and coordinated multipath transmission were combined, an incredible 99% of transmitted data packets stayed within the 500‑millisecond latency threshold commonly used for autonomous driving. When connections were tested individually, performance varied more depending on location and conditions.

The difference? A result of treating prediction, transmission control, and data handling as a single system, rather than individual parts.

A Win For Society

Autonomous driving is starting to become a thinkable part of how we operate society; its success depends on reliable communication. As it focuses on that and tries to work out what problems and issues lie ahead, NTT is taking steps to think of the practical conditions autonomous systems will face if they are to support daily life. In rural and regional areas, it's time for the robots to take the wheel.

Innovating a Sustainable Future for People and Planet

For further information, please see this link:
https://group.ntt/en/newsrelease/2025/10/08/251008a.html

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(regarding the technical demonstration)
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Daniel O'Connor

Daniel O'Connor joined the NTT Group in 1999 when he began work as the Public Relations Manager of NTT Europe. While in London, he liaised with the local press, created the company's intranet site, wrote technical copy for industry magazines and managed exhibition stands from initial design to finished displays.

Later seconded to the headquarters of NTT Communications in Tokyo, he contributed to the company's first-ever winning of global telecoms awards and the digitalisation of internal company information exchange.

Since 2015 Daniel has created content for the Group's Global Leadership Institute, the One NTT Network and is currently working with NTT R&D teams to grow public understanding of the cutting-edge research undertaken by the NTT Group.